(untitled) (Informatica, 2024)
Large-span spatial grid structures often face structural damage and defects during long-term service. To extend the lifespan of these structures and promptly detect damage and defects, this study proposes a model for structural damage identification in large-span spatial grid structures based on an improved genetic algorithm using simulated annealing optimization. Experimental results demonstrate that the hybrid intelligent algorithm’s damage identification model achieves a balanced advantage between precision and recall, with an area under the receiver operating characteristic curve reaching the highest level at 0.927. The optimization error evaluation indicators for different test functions consistently fall below 0.4, indicating superior optimization accuracy compared to other models. The genetic improvement strategy significantly enhances convergence performance for three convergence indicators, achieving a 100% convergence rate and the fastest iteration speed among the models. The damage identification model yields recognition results of 0.94 for single-member damage and 0.95 for multi-member damage, with recognition errors for other members within a reasonable range. The model can also effectively identify damage under random defects. This research enriches theoretical knowledge in the field of structural damage identification, playing a crucial role in ensuring the safety and reliability of large-span spatial grid structures.